One of the interesting aspects about huge amounts of data, such as information from electronic medical records, is that it takes the form of a complex web, characterized by relationships between each individual bit of data.

It takes researchers like Stanford’s Jure Leskovec, PhD, who specializes in the study of networks, to pick out patterns that others may have missed. Leskovec will be speaking at Stanford’s Big Data in Biomedicine Conference, which will be held May 24 and 25, and he took the time to tell me about his work via email.

How does the science of networks relate to health?

I study massive complex networks, which allows me to model large complex interconnected systems at all scales, from interactions of proteins in a cell to interactions between humans in a society…

Chronic diseases such as diabetes, cardiovascular disease, obesity, and cancer are by far the leading cause of death in the world. Rethinking the patient as a person embedded in a social network is a critical component in improving the patient’s well-being, in helping them manage crises and diseases better, and in encouraging healthy behaviors that significantly reduce the risk of many chronic diseases.

How do you do that?

The key problem is identifying causal network-based interventions that lead to sustainable change in patients’ lives. Network analysis coupled with natural experiments enable us to tackle this problem. For example, we recently established causal models for predicting which social network interventions will influence patient’s behavior the most. This research has the potential to revolutionize how we prevent and manage illness.

What will you be speaking about at Big Data in Biomedicine?

I plan to talk about how analysis of massive data can lead to new medical insights. For example, data collected by networks of cellphones and wearable devices can give us a window into monitoring and understanding physical activity and health at a planetary scale. We can discover inequality in how activity is distributed within countries and that this inequality is a better predictor of obesity prevalence in the population than average activity volume. Reduced activity in females contributes to a large portion of the observed activity inequality. We also have discovered that in more walkable cities, activity inequality is lower and activity is greater throughout the day and throughout the week, across age, gender, and body mass index (BMI) groups, with the greatest increases in activity observed for females.

Our findings have implications for global public health policy and urban planning, suggesting that activity inequality should be monitored as an indicator of underlying activity disparities. Further, the role of the built environment in increasing activity in activity poor subgroups, such as women and the elderly, warrants heightened emphasis.

Several other researchers, including Albert-László Barabási, PhD, from Northeastern University, and Trey Ideker, PhD, from the University of California, San Diego, will also address network science at the conference.